The development of technologies that allow sequencing of whole genomes provides infectious disease researchers with a greatly enhanced ability to investigate phylogenetics and transmission of bacterial pathogens by enabling discrimination at the single nucleotide polymorphism (SNP) level. Whole genome sequencing (WGS) can also provide information about other pathogen characteristics, such as antibiotic resistance and virulence traits. While WGS has become widely used in research, it has not yet transitioned into routine use to detect outbreaks of hospital-associated bacterial pathogens, which are a serious source of morbidity and mortality and contribute to increased healthcare costs. Genomic epidemiology combines the high-resolution typing information obtained by sequencing whole genomes with conventional epidemiologic information, resulting in a greatly enhanced ability to detect and track transmission events. Routine use of genomic epidemiology in hospital outbreak investigations relies partly on affordability and turnaround time, both of which have seen recent dramatic decreases. However, adoption of genomic epidemiology in the hospital setting also depends on overcoming currently existing obstacles related to validation of WGS for hospital epidemiology and difficulty of analysis of WGS data. The research proposed in this application will aid in overcoming those obstacles. The specific aims of this project are 1) t validate the ability of WGS to rapidly identify outbreaks in the hospital and 2) to develop an automated analysis system for WGS data that will generate the information needed for hospital outbreak investigation in an easily understood format. Whole genomes of isolates of four important hospital pathogens (Acinetobacter baumannii, Clostridium difficile, Staphylococcus aureus, and Klebsiella pneumoniae), obtained from multiple well-characterized hospital outbreaks, will undergo WGS. These isolates have been characterized epidemiologically and by conventional molecular typing methods. Genomic comparisons of these sequences will be correlated with the existing conventional typing data to validate the ability of WGS to detect outbreak strains and discriminate unrelated strains. The sequences will be used to develop genomic criteria to define genetically related isolates and identify outbreaks. These criteria will be utilized in the development of a WGS analysis system that will automatically generate data relevant to hospital outbreak epidemiology, producing a user-friendly results display. Input from hospital infection control and clinical microbiology personnel will contribute to the design of bot the analysis system and the results display. This study will help overcome the current obstacles to routine use of genomics for hospital outbreak detection.